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# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
pretrained=None,
backbone=dict(
type='ERFNet',
in_channels=3,
enc_downsample_channels=(16, 64, 128),
enc_stage_non_bottlenecks=(5, 8),
enc_non_bottleneck_dilations=(2, 4, 8, 16),
enc_non_bottleneck_channels=(64, 128),
dec_upsample_channels=(64, 16),
dec_stages_non_bottleneck=(2, 2),
dec_non_bottleneck_channels=(64, 16),
dropout_ratio=0.1,
init_cfg=None),
decode_head=dict(
type='FCNHead',
in_channels=16,
channels=128,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))
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